html_url,issue_url,id,node_id,user,created_at,updated_at,author_association,body,reactions,performed_via_github_app,issue
https://github.com/pydata/xarray/issues/1388#issuecomment-362951129,https://api.github.com/repos/pydata/xarray/issues/1388,362951129,MDEyOklzc3VlQ29tbWVudDM2Mjk1MTEyOQ==,1217238,2018-02-04T23:54:10Z,2018-02-04T23:54:10Z,MEMBER,"I think it would be fine to add a special case to preserve coordinates corresponding to min/max values with xarray's `min()` and `max()` methods, but I don't feel strongly about this. The exact coordinates could be surprising if there are multiple min/max values.

I agree that it does not make sense to preserve coordinates along aggregated dimensions for argmin/argmax, but we can preserve other coordinates. So I like @fujiisoup's example behavior above.

I suppose we now have two candidate APIs for returning multiple indices from a method like argmin/argmax:
1. Add an additional dimension, e.g., `argmaxdim` for keeping track of multiple indices.
2. Return a dict or Dataset with multiple indices.

I think my favorite option is (2) with `da.argmin_indices()` returning a Dataset, which will allow `da[da.argmin_indices()]` to work after we finish switching `__iter__` to only iterate over data variables (https://github.com/pydata/xarray/issues/884#issuecomment-338445322). One downside of this approach is that it is not obvious how we woudl define `argmin_indices()` to work on a Dataset, but that's probably OK given that you cannot use a Dataset (yet) for indexing.

My concern with adding an additional dimension is that it is always a little surprising and error-prone when we invent new dimension names not supplied by the user (for example, this can lead to conflicting names). Also, consolidating indices will not work as well with `idxmin()`, which might put indices of different dtypes in the same array.

Either way, I would like a separate dedicated method for returning multiple indexing arrays. It's convenient (and what users expect) for argmax to return a single array if taking the max only over one dimension. However, if we switch to add an `argmaxdim` or return a dict/Dataset for multiple dimensions, then we will end up with an annoying inconsistency between the 1D and N-D versions. It would be better to say `argmax(dim)` is only for one dimension (and raise an error if this is not true) and have the separate `argmax_indices(dims)` that is consistently defined for any number of dimensions.","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,224878728
https://github.com/pydata/xarray/issues/1388#issuecomment-298256260,https://api.github.com/repos/pydata/xarray/issues/1388,298256260,MDEyOklzc3VlQ29tbWVudDI5ODI1NjI2MA==,1217238,2017-04-30T20:50:49Z,2017-04-30T20:50:49Z,MEMBER,"I agree that `arr[arr.argmin(dim)] == arr.min(dim)` is a useful invariant, but currently xarray's indexing works a little differently from NumPy. Probably `arr.isel_points(**arr.argmin(dim)) == arg.min(dim)` is the better invariant for now, and in the future `arr[arr.argmin(dim)]` will change to work consistently (#974).

The main downside of returning a tuple or dict from `argmin()` is that it makes the common case of taking the max/min over one dimension a little harder. So possibly it would be better to write *two methods*:

- `argmin` would work like it does currently (returning an xarray object), but error if reducing over multiple dimensions.
- `argmin_indices` would return a dict suitable for use in indexing.","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,224878728
https://github.com/pydata/xarray/issues/1388#issuecomment-297845435,https://api.github.com/repos/pydata/xarray/issues/1388,297845435,MDEyOklzc3VlQ29tbWVudDI5Nzg0NTQzNQ==,1217238,2017-04-27T21:33:06Z,2017-04-27T21:33:06Z,MEMBER,"Agreed, the current implementation of `argmin()` only gives the correct result when given one dimension. It's not entirely obvious what `argmin()` should yield when done to multiple dimensions, certainly this is not very useful. I would prefer raising an error to this current behavior.","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,224878728